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README.md

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+---
+thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
+base_model: llama-moe/LLaMA-MoE-v1-3_5B-2_8
+metrics:
+- memory_disk
+- memory_inference
+- inference_latency
+- inference_throughput
+- inference_CO2_emissions
+- inference_energy_consumption
+tags:
+- pruna-ai
+---
+<!-- header start -->
+<!-- 200823 -->
+<div style="width: auto; margin-left: auto; margin-right: auto">
+    <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
+        <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+    </a>
+</div>
+<!-- header end -->
+
+[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
+[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
+[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/CP4VSgck)
+
+# Simply make AI models cheaper, smaller, faster, and greener!
+
+- Give a thumbs up if you like this model!
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
+- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
+
+## Results
+
+![image info](./plots.png)
+
+**Frequently Asked Questions**
+- ***How does the compression work?*** The model is compressed with llm-int8.
+- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
+- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
+- ***What is the model format?*** We use safetensors.
+- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
+- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
+- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
+- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
+- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
+
+## Setup
+
+You can run the smashed model with these steps:
+
+0. Check requirements from the original repo llama-moe/LLaMA-MoE-v1-3_5B-2_8 installed. In particular, check python, cuda, and transformers versions.
+1. Make sure that you have installed quantization related packages.
+    ```bash
+    pip install transformers accelerate bitsandbytes>0.37.0
+    ```
+2. Load & run the model.
+    ```python 
+   from transformers import AutoModelForCausalLM, AutoTokenizer
+   
+
+   model = AutoModelForCausalLM.from_pretrained("PrunaAI/llama-moe-LLaMA-MoE-v1-3_5B-2_8-bnb-4bit-smashed", trust_remote_code=True, device_map='auto')
+   tokenizer = AutoTokenizer.from_pretrained("llama-moe/LLaMA-MoE-v1-3_5B-2_8")
+    
+   input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
+    
+   outputs = model.generate(input_ids, max_new_tokens=216)
+   tokenizer.decode(outputs[0])
+    ```
+
+## Configurations
+
+The configuration info are in `smash_config.json`.
+
+## Credits & License
+
+The license of the smashed model follows the license of the original model. Please check the license of the original model llama-moe/LLaMA-MoE-v1-3_5B-2_8 before using this model which provided the base model. The license  of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
+
+## Want to compress other models?
+
+- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
+- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).

config.json

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+{
+  "_name_or_path": "/ceph/hdd/staff/charpent/.cache/modelsbijycn3y7u7f4q1e",
+  "add_weight_norm": false,
+  "architectures": [
+    "LlamaMoEForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_llama_moe.LlamaMoEConfig",
+    "AutoModel": "llama-moe/LLaMA-MoE-v1-3_5B-2_8--modeling_llama_moe_hf.LlamaMoEModel",
+    "AutoModelForCausalLM": "modeling_llama_moe_hf.LlamaMoEForCausalLM"
+  },
+  "bos_token_id": 1,
+  "calculator_type": "UniversalCalculator",
+  "capacity_factor": 1.25,
+  "drop_tokens": true,
+  "dropped_padding": "zero",
+  "eos_token_id": 2,
+  "gate_add_noise": true,
+  "gate_balance_loss_weight": 0.01,
+  "gate_network": "mlp",
+  "gate_noise_epsilon": 0.01,
+  "gate_type": "TopKBalancedNoisyGate",
+  "gate_use_balance": true,
+  "gate_use_softmax": true,
+  "gates": "mlp",
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "model_type": "llama_moe",
+  "multiply_gate_scores": true,
+  "num_attention_heads": 32,
+  "num_experts": 8,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "num_selects": 2,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "quantization_config": {
+    "_load_in_4bit": true,
+    "_load_in_8bit": false,
+    "bnb_4bit_compute_dtype": "bfloat16",
+    "bnb_4bit_quant_storage": "uint8",
+    "bnb_4bit_quant_type": "fp4",
+    "bnb_4bit_use_double_quant": false,
+    "llm_int8_enable_fp32_cpu_offload": false,
+    "llm_int8_has_fp16_weight": false,
+    "llm_int8_skip_modules": [
+      "lm_head"
+    ],
+    "llm_int8_threshold": 6.0,
+    "load_in_4bit": true,
+    "load_in_8bit": false,
+    "quant_method": "bitsandbytes"
+  },
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "score_scale_factor": 4.0,
+  "size_experts": [
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+  ],
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.41.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

configuration_llama_moe.py

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+from transformers.configuration_utils import PretrainedConfig
+
+
+class LlamaMoEConfig(PretrainedConfig):
+    model_type = "llama_moe"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_scaling=None,
+        # -------- moe expert configs --------
+        num_experts=16,
+        num_selects=4,
+        size_experts=None,
+        # -------- moe gate configs --------
+        gate_type="TopKBalancedNoisyGate",
+        gate_network="mlp",
+        gate_use_softmax=True,
+        gate_use_balance=True,
+        gate_balance_loss_weight=1e-2,
+        gate_add_noise=True,
+        # TopKBalancedNoisyGate
+        gate_noise_epsilon=1e-2,
+        # -------- moe calculator configs --------
+        calculator_type="UniversalCalculator",
+        multiply_gate_scores=True,
+        score_scale_factor=1.0,
+        add_weight_norm=False,
+        # SwitchDropTokenCalculator
+        drop_tokens=True,
+        dropped_padding="zero",
+        capacity_factor=1.25,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+
+        self.num_experts = num_experts
+        self.num_selects = num_selects
+        self.size_experts = size_experts
+
+        self.gate_type = gate_type
+        self.gate_network = gate_network
+        self.gate_use_softmax = gate_use_softmax
+        self.gate_use_balance = gate_use_balance
+        self.gate_balance_loss_weight = gate_balance_loss_weight
+        self.gate_add_noise = gate_add_noise
+        self.gate_noise_epsilon = gate_noise_epsilon
+
+        self.calculator_type = calculator_type
+        self.multiply_gate_scores = multiply_gate_scores
+        self.score_scale_factor = score_scale_factor
+        self.add_weight_norm = add_weight_norm
+        self.drop_tokens = drop_tokens
+        self.dropped_padding = dropped_padding
+        self.capacity_factor = capacity_factor
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if (
+            rope_scaling_factor is None
+            or not isinstance(rope_scaling_factor, float)
+            or rope_scaling_factor <= 1.0
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"
+            )

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.41.2"
+}

model-00001-of-00003.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8b9eec6f276c67605104486fafcc7fca18357819a9787fbe9ae8b6ea892d0406
+size 4992709848

model-00002-of-00003.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c5a5694eb83c3acc8c7272584b317816a62e149a8a9d175cfa43b3aedec6633f
+size 4989846240

model-00003-of-00003.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:67837a8cc010f5315999f4a166c8ff008badf4eb6d169f8e3e6bae3b44723e3a
+size 408709168

modeling_llama_moe_hf.py

@@ -0,0 +1,1664 @@
+import math
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+import torch
+import torch.utils.checkpoint
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.distributions.normal import Normal
+from transformers.modeling_outputs import (
+    CausalLMOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.activations import ACT2FN
+from transformers.utils import ModelOutput, logging
+
+from .configuration_llama_moe import LlamaMoEConfig
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "LlamaMoEConfig"
+
+
+@dataclass
+class CalculatorOutput(ModelOutput):
+    hidden_states: Optional[torch.FloatTensor] = None
+    num_dropped_tokens: Optional[int] = None
+
+
+@dataclass
+class BaseMoEModelOutputWithPast(ModelOutput):
+    """
+    Args:
+        num_dropped_tokens: layer idx to the number of dropped tokens
+    """
+
+    last_hidden_state: torch.FloatTensor = None
+    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    balance_loss: Optional[float] = None
+    num_dropped_tokens: Optional[Tuple[torch.Tensor]] = None
+    gate_load: Optional[Tuple[list]] = None
+    gate_importance: Optional[Tuple[list]] = None
+
+
+@dataclass
+class MoECausalLMOutputWithPast(CausalLMOutputWithPast):
+    balance_loss: Optional[float] = None
+    num_dropped_tokens: Optional[Tuple[int]] = None
+    gate_load: Optional[Tuple[list[torch.Tensor]]] = None
+    gate_importance: Optional[Tuple[list[torch.Tensor]]] = None
+
+
+@dataclass
+class MoEMlpOutput(ModelOutput):
+    hidden_states: Optional[torch.FloatTensor] = None
+    balance_loss: Optional[torch.FloatTensor] = None
+    num_dropped_tokens: Optional[int] = None
+    gate_load: Optional[list] = None
+    gate_importance: Optional[list] = None
+
+
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+class LlamaRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        LlamaRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+class LlamaRotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+
+class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
+    """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        t = t / self.scaling_factor
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+
+class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
+    """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class LlamaMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.pretraining_tp = config.pretraining_tp
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        if self.pretraining_tp > 1:
+            slice = self.intermediate_size // self.pretraining_tp
+            gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
+            up_proj_slices = self.up_proj.weight.split(slice, dim=0)
+            down_proj_slices = self.down_proj.weight.split(slice, dim=1)
+
+            gate_proj = torch.cat([F.linear(x, gate_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1)
+            up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1)
+
+            intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
+            down_proj = [F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.pretraining_tp)]
+            down_proj = sum(down_proj)
+        else:
+            down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+        return down_proj
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class LlamaAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: LlamaMoEConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.pretraining_tp = config.pretraining_tp
+        self.max_position_embeddings = config.max_position_embeddings
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
+                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        if self.pretraining_tp > 1:
+            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.pretraining_tp
+            query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim) // self.pretraining_tp, dim=0)
+            key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+            value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+            query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)]
+            query_states = torch.cat(query_states, dim=-1)
+
+            key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)]
+            key_states = torch.cat(key_states, dim=-1)
+
+            value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)]
+            value_states = torch.cat(value_states, dim=-1)
+
+        else:
+            query_states = self.q_proj(hidden_states)
+            key_states = self.k_proj(hidden_states)
+            value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        if self.pretraining_tp > 1:
+            attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2)
+            o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.pretraining_tp, dim=1)
+            attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.pretraining_tp)])
+        else:
+            attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class TopKBalancedNoisyGate(nn.Module):
+    def __init__(
+        self,
+        input_size,
+        num_experts,
+        num_selects,
+        gate_network="mlp",
+        use_softmax=True,
+        use_balance=True,
+        balance_loss_weight=1e-2,
+        add_noise=True,
+        noise_epsilon=1e-2,
+    ):
+        super(TopKBalancedNoisyGate, self).__init__()
+        assert num_selects <= num_experts
+        self.input_size = input_size
+        self.num_experts = num_experts
+        self.num_selects = num_selects
+
+        self.gate_network_type = gate_network
+        self.gate_network = self.get_gate_network(gate_network, input_size, num_experts)
+
+        self.use_softmax = use_softmax
+        self.softmax = nn.Softmax(1)
+
+        self.use_balance = use_balance
+        self.balance_loss_weight = balance_loss_weight
+
+        # add_noise
+        self.add_noise = add_noise
+        self.noise_epsilon = noise_epsilon
+        self.warned = False
+        if self.add_noise:
+            self.weight_noise = nn.Linear(input_size, num_experts, bias=False)
+            self.weight_noise.weight.data = torch.zeros(
+                (num_experts, input_size),
+                requires_grad=True,
+                device=self.weight_noise.weight.data.device,
+                dtype=self.weight_noise.weight.data.dtype,
+            )
+            self.mean = 0.0
+            self.std = 1.0
+            self.normal = Normal(self.mean, self.std)
+            self.softplus = nn.Softplus()
+
+        self.reset_parameters()
+
+    def get_gate_network(self, gate_type, input_size, num_experts):
+        gate_type = gate_type.lower()
+
+        if gate_type == "linear":
+            gate_network = nn.Linear(input_size, num_experts, bias=False)
+            nn.init.zeros_(gate_network.weight)
+        elif gate_type == "mlp":
+            gate_network = torch.nn.Sequential(
+                torch.nn.Linear(input_size, num_experts, bias=False),
+                torch.nn.Tanh(),
+                torch.nn.Linear(num_experts, num_experts, bias=False),
+            )
+        else:
+            raise ValueError(f'Unexpected gate_type: {gate_type}.')
+
+        return gate_network
+
+    def reset_gate_network(self):
+        if "gate_network_type" not in vars(self):
+            raise KeyError(f"{type(self)} does not have a gate network.")
+        else:
+            self.gate_network = self.get_gate_network(
+                self.gate_network_type, self.input_size, self.num_experts
+            )
+
+    def reset_parameters(self):
+        if self.add_noise:
+            nn.init.zeros_(self.weight_noise.weight)
+            # nn.init.zeros_(self.weight_noise)
+
+    def cv_squared(self, x, eps=1e-10):
+        """The squared coefficient of variation of a sample.
+        Useful as a loss to encourage a positive distribution to be more uniform.
+        Epsilons added for numerical stability.
+        Returns 0 for an empty Tensor.
+        Args:
+        x: a `Tensor`.
+        Returns:
+        a `Scalar`.s
+        """
+        if x.shape[0] == 1:
+            return torch.tensor(0.0, device=x.device)
+        return x.float().var() / (x.float().mean() ** 2 + eps)
+
+    def forward(self, x):
+        logits_gate = self.gate_network(x)
+        if self.training and self.add_noise:
+            noise_mm = self.weight_noise(x)
+            noise_control = self.softplus(noise_mm) + self.noise_epsilon
+            logits_noise = torch.randn_like(logits_gate) * noise_control
+            logits = logits_gate + logits_noise
+        else:
+            logits = logits_gate
+
+        top_logits, top_indices = logits.topk(min(self.num_selects + 1, self.num_experts), dim=1)  # 选择并排序前k+1个权重
+        top_k_logits = top_logits[:, :self.num_selects]
+        top_k_indices = top_indices[:, :self.num_selects]
+        top_k_scores = self.softmax(top_k_logits.to(torch.float32)) if self.use_softmax else top_k_logits
+        top_k_scores = top_k_scores.to(logits.dtype)
+
+        zeros = torch.zeros_like(logits, requires_grad=True, device=logits.device)
+        scores_filtered = zeros.scatter(dim=1, index=top_k_indices, src=top_k_scores)  # shape(batch_size, num_experts)
+        importance = scores_filtered.sum(0)  # shape(num_experts)
+
+        if self.training:
+            if self.add_noise and self.num_selects != self.num_experts:
+                batch_size = top_logits.size(0)
+                m = top_logits.size(1)
+                top_values_flat = top_logits.flatten()
+                threshold_positions_if_in = torch.arange(batch_size, device=x.device) * m + self.num_selects
+                threshold_if_in = torch.unsqueeze(torch.gather(top_values_flat, 0, threshold_positions_if_in), 1)
+                is_in = torch.gt(logits_noise, threshold_if_in)
+                threshold_positions_if_out = threshold_positions_if_in - 1
+                threshold_if_out = torch.unsqueeze(torch.gather(top_values_flat, 0, threshold_positions_if_out), 1)
+                # is each value currently in the top k.
+                prob_if_in = self.normal.cdf((logits_gate - threshold_if_in) / noise_control)
+                prob_if_out = self.normal.cdf((logits_gate - threshold_if_out) / noise_control)
+                prob = torch.where(is_in, prob_if_in, prob_if_out)
+                load = prob.sum(0)
+            else:
+                load = (scores_filtered > 0).sum(0)
+                if not self.add_noise and not self.warned:
+                    warnings.warn('Gradient-trackable implementation for load calculation is only available when "add_noise=True". '
+                                  'Training without noise will block the gradient from "load" path and lead to inconsistency in optimization objectives.')
+                    self.warned = True
+        else:
+            load = (scores_filtered > 0).sum(0)
+
+        if self.use_balance:
+            balance_loss = self.cv_squared(importance) + self.cv_squared(load)
+            balance_loss *= self.balance_loss_weight
+        else:
+            balance_loss = torch.tensor(-100.0, device=x.device)
+
+        return {
+            "topK_indices": top_k_indices,
+            "topK_scores": top_k_scores,
+            "balance_loss": balance_loss,
+            "load": load,
+            "importance": importance,
+        }
+
+
+class LinearGLUExperts(nn.Module):
+    """
+    Modified from transformers.models.llama.modeling_llama.LlamaMLP
+    """
+
+    __constants__ = [
+        "bias",
+        "in_features",
+        "hidden_features",
+        "out_features",
+        "hidden_act",
+        "num_experts",
+        "size_experts",
+    ]
+
+    def __init__(
+        self,
+        in_features,
+        hidden_features,
+        out_features,
+        hidden_act,
+        num_experts,
+        size_experts=None,
+        bias=True,
+        device=None,
+        dtype=None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super(LinearGLUExperts, self).__init__()
+        self.in_features = in_features
+        self.hidden_features = hidden_features
+        self.out_features = out_features
+        self.hidden_act = hidden_act
+        self.num_experts = num_experts
+
+        if size_experts is None:
+            # all experts share the same number of hidden neurons
+            assert hidden_features % num_experts == 0
+            size_per_expert = hidden_features // num_experts
+            size_experts = [size_per_expert for _ in range(num_experts)]
+        else:
+            # use specified expert sizes
+            assert (
+                len(size_experts) == num_experts
+                and sum(size_experts) == hidden_features
+            )
+        self.size_experts = size_experts
+
+        self.act_fn = ACT2FN[hidden_act]
+
+        self.weight_gate = nn.ParameterList()
+        self.weight_up = nn.ParameterList()
+        self.weight_down = nn.ParameterList()
+
+        for i in range(num_experts):
+            # this matrix will be transposed when performing linear forwarding
+            this_expert_weight_gate = nn.Parameter(
+                torch.empty((size_experts[i], in_features), **factory_kwargs)
+            )
+            # this matrix will be transposed when performing linear forwarding
+            this_expert_weight_up = nn.Parameter(
+                torch.empty((size_experts[i], in_features), **factory_kwargs)
+            )
+            # this matrix will be transposed when performing linear forwarding
+            this_expert_weight_down = nn.Parameter(
+                torch.empty((out_features, size_experts[i]), **factory_kwargs)
+            )
+            self.weight_gate.append(this_expert_weight_gate)
+            self.weight_up.append(this_expert_weight_up)
+            self.weight_down.append(this_expert_weight_down)
+
+        if bias:
+            self.bias_gate = nn.ParameterList()
+            self.bias_up = nn.ParameterList()
+            self.bias_down = nn.ParameterList()
+
+            for i in range(num_experts):
+                this_expert_bias_gate = nn.Parameter(
+                    torch.empty((size_experts[i],), **factory_kwargs)
+                )
+                this_expert_bias_up = nn.Parameter(
+                    torch.empty((size_experts[i],), **factory_kwargs)
+                )
+                this_expert_bias_down = nn.Parameter(
+                    torch.empty((out_features,), **factory_kwargs)
+                )
+                self.bias_gate.append(this_expert_bias_gate)
+                self.bias_up.append(this_expert_bias_up)
+                self.bias_down.append(this_expert_bias_down)
+        else:
+            self.register_parameter("bias_gate", None)
+            self.register_parameter("bias_up", None)
+            self.register_parameter("bias_down", None)
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        for i in range(self.num_experts):
+            nn.init.kaiming_uniform_(self.weight_gate[i], a=math.sqrt(5))
+            nn.init.kaiming_uniform_(self.weight_up[i], a=math.sqrt(5))
+            nn.init.kaiming_uniform_(self.weight_down[i], a=math.sqrt(5))
+            if self.bias_gate is not None:
+                fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight_gate[i])
+                bound = 1 / math.sqrt(fan_in)
+                nn.init.uniform_(self.bias_gate[i], -bound, bound)
+            if self.bias_up is not None:
+                fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight_up[i])
+                bound = 1 / math.sqrt(fan_in)
+                nn.init.uniform_(self.bias_up[i], -bound, bound)
+            if self.bias_down is not None:
+                fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight_down[i])
+                bound = 1 / math.sqrt(fan_in)
+                nn.init.uniform_(self.bias_down[i], -bound, bound)
+
+    def forward(self, input, i):
+        gate = self.act_fn(
+            F.linear(
+                input,
+                self.weight_gate[i],
+                self.bias_gate[i] if self.bias_gate is not None else None,
+            )
+        )
+        up = F.linear(
+            input,
+            self.weight_up[i],
+            self.bias_up[i] if self.bias_up is not None else None,
+        )
+        down = F.linear(
+            gate * up,
+            self.weight_down[i],
+            self.bias_down[i] if self.bias_down is not None else None,
+        )
+        return down
+
+    def extra_repr(self):
+        return (
+            "in_features={}, hidden_features={}, out_features={}, hidden_act={},"
+            " num_experts={}, size_experts={}, bias={}".format(
+                self.in_features,
+                self.hidden_features,
+                self.out_features,
+                self.hidden_act,
+                self.num_experts,
+                self.size_experts,
+                self.bias_gate is not None,
+            )
+        )
+
+
+class UniversalCalculator(nn.Module):
+    def __init__(
+        self,
+        experts: LinearGLUExperts,
+        multiply_gate_scores=True,
+        score_scale_factor=1.0,
+        add_weight_norm: bool = False,
+    ):
+        super(UniversalCalculator, self).__init__()
+        self.experts = experts
+        # TODO (zhutong): use vmap to boost the training efficiency
+        # self.experts_vmap = torch.vmap(self.experts)
+        self.multiply_gate_scores = multiply_gate_scores
+        self.score_scale_factor = score_scale_factor
+        self.num_experts = experts.num_experts
+        self.mlp_norm = None
+        if multiply_gate_scores and add_weight_norm:
+            raise NotImplementedError
+
+    def reset_experts(self):
+        self.experts.reset_parameters()
+
+    def forward(
+        self, x, topK_indices, topK_scores, expert_batch_size=None, **kwargs
+    ) -> CalculatorOutput:
+        batch_size = topK_indices.size(0)  # topK_indices: (bsz*seq_len, num_selects)
+        num_selects = topK_indices.size(1)
+        topK_indices = topK_indices.flatten()  # shape(batch_size*num_selects)
+        topK_scores = topK_scores.flatten()  # shape(batch_size*num_selects)
+        batch_indices = torch.arange(
+            batch_size, device=topK_scores.device
+        ).repeat_interleave(num_selects)
+
+        _, index_sorted_topK_indices = topK_indices.sort(0)
+
+        sorted_topK_scores = topK_scores.index_select(0, index_sorted_topK_indices)
+        sorted_batch_indices = batch_indices.index_select(0, index_sorted_topK_indices)
+
+        if expert_batch_size is None:
+            expert_batch_size = topK_indices.bincount(
+                minlength=self.num_experts
+            ).tolist()
+
+        sorted_x = x.index_select(0, sorted_batch_indices)
+        split_x = torch.split(sorted_x, expert_batch_size, dim=0)
+
+        expert_outputs = [
+            self.experts(split_x[i], i)
+            for i in range(self.num_experts)
+            if split_x[i].shape[0] > 0
+        ]
+
+        # (bsz*seq_len*num_selects, hidden_size)
+        cat_expert_outputs = torch.cat(expert_outputs, 0)
+        output_dim = cat_expert_outputs.size(1)
+        if self.multiply_gate_scores:
+            if self.mlp_norm is None:
+                cat_expert_outputs = torch.mul(
+                    cat_expert_outputs,
+                    sorted_topK_scores.reshape(-1, 1) * self.score_scale_factor,
+                )
+                # cat_expert_outputs = torch.mul(cat_expert_outputs, sorted_topK_scores.reshape(-1, 1) * 1.0)
+            else:
+                cat_expert_outputs = torch.mul(
+                    cat_expert_outputs, sorted_topK_scores.reshape(-1, 1)
+                )
+                cat_expert_outputs = self.mlp_norm(cat_expert_outputs)
+
+        zeros = torch.zeros(
+            (batch_size, output_dim),
+            device=cat_expert_outputs.device,
+            dtype=cat_expert_outputs.dtype,
+        )
+        y = zeros.index_add(0, sorted_batch_indices, cat_expert_outputs)
+
+        return CalculatorOutput(hidden_states=y, num_dropped_tokens=torch.tensor(-1.0))
+
+
+class BaseMoELayer(nn.Module):
+    def __init__(self):
+        super(BaseMoELayer, self).__init__()
+
+        self.gate: TopKBalancedNoisyGate
+        self.calculator: UniversalCalculator
+
+    def _create_gate(self, **kwargs):
+        self.gate_type = kwargs.get("gate_type", "TopKBalancedNoisyGate")
+
+        if self.gate_type == "TopKBalancedNoisyGate":  # noisy gate
+            self.gate = TopKBalancedNoisyGate(
+                self.input_size,
+                self.num_experts,
+                self.num_selects,
+                gate_network=kwargs.get("gate_network", "mlp"),
+                use_softmax=kwargs.get("gate_use_softmax", True),
+                use_balance=kwargs.get("gate_use_balance", True),
+                balance_loss_weight=kwargs.get("gate_balance_loss_weight", 1e-2),
+                add_noise=kwargs.get("gate_add_noise", True),
+                noise_epsilon=kwargs.get("gate_noise_epsilon", 1e-2),
+            )
+        else:
+            raise NotImplementedError
+
+    def _create_calculator(self, experts, **kwargs):
+        self.calculator_type = kwargs.get("calculator_type", "UniversalCalculator")
+
+        if self.calculator_type == "UniversalCalculator":  # top K calculator
+            self.calculator = UniversalCalculator(
+                experts,
+                multiply_gate_scores=kwargs.get("multiply_gate_scores", True),
+                score_scale_factor=kwargs.get("score_scale_factor", 1.0),
+                add_weight_norm=kwargs.get("add_weight_norm", False),
+            )
+        else:
+            raise NotImplementedError
+
+    def forward(self, x) -> MoEMlpOutput:
+        original_shape = x.shape[:-1]
+        x = x.reshape(-1, self.input_size)
+        gate_outputs: dict = self.gate(x)
+        calc_outs: CalculatorOutput = self.calculator(x, **gate_outputs)
+        y = calc_outs.hidden_states
+        y = y.reshape(original_shape + (self.output_size,))
+
+        return MoEMlpOutput(
+            hidden_states=y,
+            balance_loss=gate_outputs.get("balance_loss"),
+            num_dropped_tokens=calc_outs.num_dropped_tokens,
+            gate_load=gate_outputs.get("load", torch.tensor(-1)),
+            gate_importance=gate_outputs.get("importance", torch.tensor(-1)),
+        )
+
+    def set_num_selects(self, num_selects):
+        if "num_selects" not in vars(self.gate):
+            raise KeyError(f'{self.gate_type} does not have a key named "num_selects".')
+        elif num_selects > self.gate.num_experts:
+            raise ValueError(
+                'The value of "num_selects" must satisfy "num_selects <= num_experts"!'
+            )
+        elif self.gate_type in ("SwitchBalancedGate",):
+            raise ValueError(
+                f"{self.gate_type} doesn't support manually setting num_selects."
+            )
+        else:
+            self.num_selects = num_selects
+            self.gate.num_selects = num_selects
+
+    def set_gate_use_softmax(self, use_softmax):
+        if "use_softmax" not in vars(self.gate):
+            raise KeyError(f'{self.gate_type} does not have a key named "use_softmax".')
+        else:
+            self.gate.use_softmax = use_softmax
+
+    def set_gate_use_balance(self, use_balance):
+        if "use_balance" not in vars(self.gate):
+            raise KeyError(f'{self.gate_type} does not have a key named "use_balance".')
+        else:
+            self.gate.use_balance = use_balance
+
+    def set_gate_balance_loss_weight(self, balance_loss_weight):
+        if "balance_loss_weight" not in vars(self.gate):
+            raise KeyError(
+                f'{self.gate_type} does not have a key named "balance_loss_weight".'
+            )
+        else:
+            self.gate.balance_loss_weight = balance_loss_weight
+
+    def set_gate_add_noise(self, add_noise):
+        if "add_noise" not in vars(self.gate):
+            raise KeyError(f'{self.gate_type} does not have a key named "add_noise".')
+        else:
+            self.gate.add_noise = add_noise
+
+    def set_gate_noise_epsilon(self, noise_epsilon):
+        if "noise_epsilon" not in vars(self.gate):
+            raise KeyError(
+                f'{self.gate_type} does not have a key named "noise_epsilon".'
+            )
+        else:
+            self.gate.noise_epsilon = noise_epsilon
+
+    def set_calculator_multiply_gate_scores(self, multiply_gate_scores):
+        if "multiply_gate_scores" not in vars(self.calculator):
+            raise KeyError(
+                f'{self.gate_type} does not have a key named "multiply_gate_scores".'
+            )
+        else:
+            self.calculator.multiply_gate_scores = multiply_gate_scores
+
+    def set_calculator_score_scale_factor(self, score_scale_factor):
+        if "score_scale_factor" not in vars(self.calculator):
+            raise KeyError(
+                f'{self.gate_type} does not have a key named "score_scale_factor".'
+            )
+        else:
+            self.calculator.score_scale_factor = score_scale_factor
+
+    def set_calculator_drop_tokens(self, drop_tokens):
+        if "drop_tokens" not in vars(self.calculator):
+            raise KeyError(f'{self.gate_type} does not have a key named "drop_tokens".')
+        elif (
+            drop_tokens
+            and self.calculator.dropped_padding != "zero"
+            and self.input_size != self.output_size
+        ):
+            warnings.warn(
+                'Setting "drop_tokens=True" without zero dropped padding when "input_size != output_size" will cause error!'
+            )
+        else:
+            self.calculator.drop_tokens = drop_tokens
+
+    def set_calculator_dropped_padding(self, dropped_padding):
+        if "dropped_padding" not in vars(self.calculator):
+            raise KeyError(
+                f'{self.gate_type} does not have a key named "dropped_padding".'
+            )
+        elif dropped_padding not in self.calculator.available_dropped_padding_choices:
+            raise ValueError(
+                f"'dropped_padding' type not available! (available choices: {self.calculator.available_dropped_padding_choices})"
+            )
+        elif (
+            self.calculator.drop_tokens
+            and dropped_padding != "zero"
+            and self.input_size != self.output_size
+        ):
+            warnings.warn(
+                f'Setting "dropped_padding={dropped_padding}" with "drop_tokens=True" when "input_size != output_size" will cause error!'
+            )
+        else:
+            self.calculator.dropped_padding = dropped_padding
+
+    def set_calculator_capacity_factor(self, capacity_factor):
+        if "capacity_factor" not in vars(self.calculator):
+            raise KeyError(
+                f'{self.gate_type} does not have a key named "capacity_factor".'
+            )
+        else:
+            self.calculator.capacity_factor = capacity_factor
+
+    def reset_gate_network(self):
+        self.gate.reset_gate_network()
+
+    def reset_experts(self):
+        self.calculator.reset_experts()
+
+
+class LinearGLUMoELayer(BaseMoELayer):
+    def __init__(
+        self,
+        input_size,
+        hidden_size,
+        output_size,
+        hidden_act,
+        num_experts,
+        num_selects,
+        size_experts=None,
+        bias=True,
+        **kwargs,
+    ):
+        super(LinearGLUMoELayer, self).__init__()
+        assert num_selects <= num_experts
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.output_size = output_size
+        self.hidden_act = hidden_act
+        self.num_experts = num_experts
+        self.num_selects = num_selects
+        self.size_experts = size_experts
+        self.bias = bias
+
+        experts = LinearGLUExperts(
+            input_size,
+            hidden_size,
+            output_size,
+            hidden_act,
+            num_experts,
+            size_experts=size_experts,
+            bias=bias,
+        )
+
+        self._create_gate(**kwargs)
+        self._create_calculator(experts, **kwargs)
+
+
+class LlamaMoEDecoderLayer(nn.Module):
+    def __init__(self, config: LlamaMoEConfig, layer_index):
+        super().__init__()
+
+        self.hidden_size = config.hidden_size
+        self.self_attn = LlamaAttention(config=config)
+        self.mlp = LlamaMLP(config)
+        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        gating_config = {
+            # all gates
+            "gate_type": config.gate_type,
+            "gate_network": config.gate_network,
+            "gate_use_softmax": config.gate_use_softmax,
+            "gate_use_balance": config.gate_use_balance,
+            "gate_balance_loss_weight": config.gate_balance_loss_weight,
+            "gate_add_noise": config.gate_add_noise,
+            # TopKBalancedNoisyGate
+            "gate_noise_epsilon": config.gate_noise_epsilon,
+        }
+        calculator_config = {
+            # all calculators
+            "calculator_type": config.calculator_type,
+            "multiply_gate_scores": config.multiply_gate_scores,
+            "score_scale_factor": (
+                config.score_scale_factor[layer_index]
+                if isinstance(config.score_scale_factor, list)
+                else config.score_scale_factor
+            ),
+            "add_weight_norm": config.add_weight_norm,
+            # SwitchDropTokenCalculator
+            "drop_tokens": config.drop_tokens,
+            "dropped_padding": config.dropped_padding,
+            "capacity_factor": config.capacity_factor,
+        }
+
+        self.mlp = LinearGLUMoELayer(
+            input_size=self.hidden_size,
+            hidden_size=config.intermediate_size,
+            output_size=self.hidden_size,
+            hidden_act=config.hidden_act,
+            num_experts=config.num_experts,
+            num_selects=config.num_selects,
+            size_experts=(
+                config.size_experts[layer_index]
+                if config.size_experts is not None
+                else None
+            ),
+            bias=False,
+            **gating_config,
+            **calculator_config,
+        )
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        position_ids=None,
+        past_key_value=None,
+        output_attentions=False,
+        use_cache=False,
+    ) -> tuple:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        mlp_outs: MoEMlpOutput = self.mlp(hidden_states)
+        hidden_states = residual + mlp_outs.hidden_states
+
+        outputs = (
+            hidden_states,
+            mlp_outs.balance_loss,
+            mlp_outs.num_dropped_tokens,
+            mlp_outs.gate_load,
+            mlp_outs.gate_importance,
+        )
+        if output_attentions:
+            outputs += (self_attn_weights,)
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+    def set_moe_num_selects(self, num_selects):
+        self.mlp.set_num_selects(num_selects)
+
+    def set_moe_gate_use_softmax(self, use_softmax):
+        self.mlp.set_gate_use_softmax(use_softmax)
+
+    def set_moe_gate_use_balance(self, use_balance):
+        self.mlp.set_gate_use_balance(use_balance)
+
+    def set_moe_gate_balance_loss_weight(self, balance_loss_weight):
+        self.mlp.set_gate_balance_loss_weight(balance_loss_weight)
+
+    def set_moe_gate_add_noise(self, add_noise):
+        self.mlp.set_gate_add_noise(add_noise)
+
+    def set_moe_gate_noise_epsilon(self, noise_epsilon):
+        self.mlp.set_gate_noise_epsilon(noise_epsilon)
+
+    def set_moe_calculator_multiply_gate_scores(self, multiply_gate_scores):
+        self.mlp.set_calculator_multiply_gate_scores(multiply_gate_scores)
+
+    def set_moe_calculator_score_scale_factor(self, score_scale_factor):
+        self.mlp.set_calculator_score_scale_factor(score_scale_factor)
+
+    def set_moe_calculator_drop_tokens(self, drop_tokens):
+        self.mlp.set_calculator_drop_tokens(drop_tokens)
+
+    def set_moe_calculator_dropped_padding(self, dropped_padding):
+        self.mlp.set_calculator_dropped_padding(dropped_padding)
+
+    def set_moe_calculator_capacity_factor(self, capacity_factor):
+        self.mlp.set_calculator_capacity_factor(capacity_factor)
+
+    def reset_gate_network(self):
+        self.mlp.reset_gate_network()
+
+    def reset_experts(self):
+        self.mlp.reset_experts()
+
+
+class LlamaMoEPreTrainedModel(PreTrainedModel):
+    config_class = LlamaMoEConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["LlamaMoEDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, LlamaMoEModel):
+            module.gradient_checkpointing = value
+
+
+class LlamaMoEModel(LlamaMoEPreTrainedModel):
+    def __init__(self, config: LlamaMoEConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [LlamaMoEDecoderLayer(config, i) for i in range(config.num_hidden_layers)]
+        )
+        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        past_key_values=None,
+        inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both decoder_input_ids and decoder_inputs_embeds at"
+                " the same time"
+            )
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError(
+                "You have to specify either decoder_input_ids or decoder_inputs_embeds"
+            )
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length,
+                seq_length + past_key_values_length,
+                dtype=torch.long,
+                device=device,
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        # embed positions
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past),
+                dtype=torch.bool,
+                device=inputs_embeds.device,
+            )
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask,
+            (batch_size, seq_length),
+            inputs_embeds,
+            past_key_values_length,
+        )
+
+        hidden_states = inputs_embeds
+        balance_loss = 0.0
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing."
+                    " Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        num_dropped_tokens = ()
+        gate_load = ()
+        gate_importance = ()
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = (
+                past_key_values[idx] if past_key_values is not None else None
+            )
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs: tuple = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs: tuple = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+            if layer_outputs[1] is not None:
+                balance_loss += layer_outputs[1]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[6 if output_attentions else 5],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[5],)
+
+            num_dropped_tokens += (layer_outputs[2],)
+            gate_load += (layer_outputs[3],)
+            gate_importance += (layer_outputs[4],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
+            )
+        return BaseMoEModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            balance_loss=balance_loss,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+            num_dropped_tokens=num_dropped_tokens,
+            gate_load=gate_load,
+            gate_importance=gate_importance,
+        )
+
+    def update_config(self):
+        self.config.vocab_size = self.config.vocab_size
+        self.config.max_position_embeddings = self.config.max_position_embeddings
+        # ↓↓↓↓↓↓↓↓↓↓↓↓ changed here ↓↓↓↓↓↓↓↓↓↓↓↓ #
+        self.config.hidden_size = self.layers[0].mlp.input_size
+        self.config.intermediate_size = self.layers[0].mlp.hidden_size
+        self.config.num_hidden_layers = len(self.layers)
+        self.config.num_attention_heads = self.layers[0].self_attn.num_heads
+        self.config.hidden_act = self.layers[0].mlp.hidden_act
+        # ↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑ #
+        self.config.initializer_range = self.config.initializer_range
+        self.config.rms_norm_eps = self.config.rms_norm_eps
+        self.config.pretraining_tp = self.config.pretraining_tp
+        self.config.use_cache = self.config.use_cache
+        self.config.rope_scaling = self.config.rope_scaling
+        self.config._rope_scaling_validation()
+
+        self.config.num_experts = self.layers[0].mlp.num_experts
+        self.config.num_selects = self.layers[0].mlp.num_selects
+        self.config.size_experts = [
+            self.layers[i].mlp.calculator.experts.size_experts
+            for i in range(self.config.num_hidden_layers)
+        ]
+
+        self.config.gate_type = vars(self.layers[0].mlp).get(
+            "gate_type", "TopKBalancedNoisyGate"
+        )
+        self.config.gate_network = vars(self.layers[0].mlp.gate).get(
+            "gate_network_type", "mlp"
+        )
+        self.config.gate_use_softmax = vars(self.layers[0].mlp.gate).get(
+            "use_softmax", True
+        )
+        self.config.gate_use_balance = vars(self.layers[0].mlp.gate).get(
+            "use_balance", True
+        )
+        self.config.gate_balance_loss_weight = vars(self.layers[0].mlp.gate).get(
+            "balance_loss_weight", 1e-2
+        )
+        self.config.gate_add_noise = vars(self.layers[0].mlp.gate).get(
+            "add_noise", True
+        )
+        self.config.gate_noise_epsilon = vars(self.layers[0].mlp.gate).get(
+            "noise_epsilon", 1e-2
+        )
+
+        self.config.calculator_type = vars(self.layers[0].mlp).get(
+            "calculator_type", "UniversalCalculator"
+        )
+        self.config.multiply_gate_scores = vars(self.layers[0].mlp.calculator).get(
+            "multiply_gate_scores", True
+        )
+        self.config.score_scale_factor = [
+            vars(self.layers[i].mlp.calculator).get("score_scale_factor", 1.0)
+            for i in range(self.config.num_hidden_layers)
+        ]
+        self.config.drop_tokens = vars(self.layers[0].mlp.calculator).get(
+            "drop_tokens", True
+        )
+        self.config.dropped_padding = vars(self.layers[0].mlp.calculator).get(
+            "dropped_padding", "zero"
+        )
+        self.config.capacity_factor = vars(self.layers[0].mlp.calculator).get(
+            "capacity_factor", 1.25
+        )
+
+    def set_moe_num_selects(self, num_selects):
+        for idx, decoder_layer in enumerate(self.layers):
+            decoder_layer.set_moe_num_selects(num_selects)
+
+    def set_moe_gate_use_softmax(self, use_softmax):
+        for idx, decoder_layer in enumerate(self.layers):
+            decoder_layer.set_moe_gate_use_softmax(use_softmax)
+
+    def set_moe_gate_use_balance(self, use_balance):
+        for idx, decoder_layer in enumerate(self.layers):
+            decoder_layer.set_moe_gate_use_balance(use_balance)
+
+    def set_moe_gate_balance_loss_weight(self, balance_loss_weight):
+        for idx, decoder_layer in enumerate(self.layers):
+            decoder_layer.set_moe_gate_balance_loss_weight(balance_loss_weight)
+
+    def set_moe_gate_add_noise(self, add_noise):
+        for idx, decoder_layer in enumerate(self.layers):
+            decoder_layer.set_moe_gate_add_noise(add_noise)
+
+    def set_moe_gate_noise_epsilon(self, noise_epsilon):
+        for idx, decoder_layer in enumerate(self.layers):
+            decoder_layer.set_moe_gate_noise_epsilon(noise_epsilon)
+
+    def set_moe_calculator_multiply_gate_scores(self, multiply_gate_scores):
+        for idx, decoder_layer in enumerate(self.layers):
+            decoder_layer.set_moe_calculator_multiply_gate_scores(multiply_gate_scores)
+
+    def set_moe_calculator_score_scale_factor(
+        self, score_scale_factor, layer_index=None
+    ):
+        if layer_index is None:
+            for idx, decoder_layer in enumerate(self.layers):
+                decoder_layer.set_moe_calculator_score_scale_factor(score_scale_factor)
+        else:
+            self.layers[layer_index].set_moe_calculator_score_scale_factor(
+                score_scale_factor
+            )
+
+    def set_moe_calculator_drop_tokens(self, drop_tokens):
+        for idx, decoder_layer in enumerate(self.layers):
+            decoder_layer.set_moe_calculator_drop_tokens(drop_tokens)
+
+    def set_moe_calculator_dropped_padding(self, dropped_padding):
+        for idx, decoder_layer in enumerate(self.layers):
+            decoder_layer.set_moe_calculator_dropped_padding(dropped_padding)
+
+    def set_moe_calculator_capacity_factor(self, capacity_factor):
+        for idx, decoder_layer in enumerate(self.layers):
+            decoder_layer.set_moe_calculator_capacity_factor(capacity_factor)
+
+    def reset_gate_network(self):
+        for idx, decoder_layer in enumerate(self.layers):
+            decoder_layer.reset_gate_network()
+
+    def reset_experts(self):
+        for idx, decoder_layer in enumerate(self.layers):
+            decoder_layer.reset_experts()
+
+
+class LlamaMoEForCausalLM(LlamaMoEPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = LlamaMoEModel(config)
+        self.pretraining_tp = config.pretraining_tp
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        past_key_values=None,
+        inputs_embeds=None,
+        labels=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs,
+    ):
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs: BaseMoEModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+            if outputs.balance_loss is not None and outputs.balance_loss > 0:
+                loss += outputs.balance_loss
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return MoECausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            num_dropped_tokens=outputs.num_dropped_tokens,
+            balance_loss=outputs.balance_loss,
+            gate_load=outputs.gate_load,
+            gate_importance=outputs.gate_importance,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+    def update_config(self):
+        self.model.update_config()
+
+    def set_moe_num_selects(self, num_selects):
+        self.model.set_moe_num_selects(num_selects)
+
+    def set_moe_gate_use_softmax(self, use_softmax):
+        self.model.set_moe_gate_use_softmax(use_softmax)
+
+    def set_moe_gate_use_balance(self, use_balance):
+        self.model.set_moe_gate_use_balance(use_balance)
+
+    def set_moe_gate_balance_loss_weight(self, balance_loss_weight):
+        self.model.set_moe_gate_balance_loss_weight(balance_loss_weight)
+
+    def set_moe_gate_add_noise(self, add_noise):
+        self.model.set_moe_gate_add_noise(add_noise)
+
+    def set_moe_gate_noise_epsilon(self, noise_epsilon):
+        self.model.set_moe_gate_noise_epsilon(noise_epsilon)
+
+    def set_moe_calculator_multiply_gate_scores(self, multiply_gate_scores):
+        self.model.set_moe_calculator_multiply_gate_scores(multiply_gate_scores)
+
+    def set_moe_calculator_score_scale_factor(
+        self, score_scale_factor, layer_index=None
+    ):
+        self.model.set_moe_calculator_score_scale_factor(
+            score_scale_factor, layer_index=layer_index
+        )
+
+    def set_moe_calculator_drop_tokens(self, drop_tokens):
+        self.model.set_moe_calculator_drop_tokens(drop_tokens)
+
+    def set_moe_calculator_dropped_padding(self, dropped_padding):
+        self.model.set_moe_calculator_dropped_padding(dropped_padding)
+
+    def set_moe_calculator_capacity_factor(self, capacity_factor):
+        self.model.set_moe_calculator_capacity_factor(capacity_factor)
+
+    def reset_gate_network(self):
+        self.model.reset_gate_network()
+
+    def reset_experts(self):
+        self.model.reset_experts()

smash_config.json

@@ -0,0 +1,31 @@
+{
+    "api_key": null,
+    "verify_url": "http://johnrachwan.pythonanywhere.com",
+    "smash_config": {
+        "pruners": "None",
+        "pruning_ratio": 0.0,
+        "factorizers": "None",
+        "quantizers": "['llm-int8']",
+        "weight_quantization_bits": 4,
+        "output_deviation": 0.005,
+        "compilers": "None",
+        "static_batch": true,
+        "static_shape": true,
+        "controlnet": "None",
+        "unet_dim": 4,
+        "device": "cuda",
+        "cache_dir": "/ceph/hdd/staff/charpent/.cache/modelsbijycn3y",
+        "batch_size": 1,
+        "model_name": "llama-moe/LLaMA-MoE-v1-3_5B-2_8",
+        "task": "text_text_generation",
+        "max_batch_size": 1,
+        "qtype_weight": "torch.qint8",
+        "qtype_activation": "torch.quint8",
+        "qobserver": "<class 'torch.ao.quantization.observer.MinMaxObserver'>",
+        "qscheme": "torch.per_tensor_symmetric",
+        "qconfig": "x86",
+        "group_size": 128,
+        "damp_percent": 0.1,
+        "save_load_fn": "bitsandbytes"
+    }
+}

special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,44 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false,
+  "use_fast": true
+}